Methodologies for analyzing the forces that move and shape national economies have advanced markedly in the last thirty years, enabling economists as never before to unite theoretical and empirical research and align measurement with theory. In <i>Structural Macroeconometrics</i>, David DeJong and...

This paper investigates the relative importance of shocks to total factor productivity (TFP) versus the marginal efficiency of investment (MEI) in explaining cyclical variations. The literature offers contrasting results: TFP shocks are important in neoclassical environments, while relatively...

Methodologies for analyzing the forces that move and shape national economies have advanced markedly in the last thirty years, enabling economists as never before to unite theoretical and empirical research and align measurement with theory. In <i>Structural Macroeconometrics</i>, David DeJong and...

We examine the role of generalized constant gain stochastic gradient (SGCG) learning in generating large deviations of an endogenous variable from its rational expectations value. We show analytically that these large deviations can occur with a frequency associated with a fat tailed...

We quantitatively evaluate a business-cycle environment featuring endogenous capital utilization and nominal price rigidity that illustrates a negative relationship between labor hours and technology (TFP) shocks and a positive relationship between hours and investment (MEI) shocks. Sticky...

We axiomatize decreasing impatience (DI) in a discrete-time setting, as originally discussed by Prelec, and formulate a class of recursively-defined discounting functions that conform to DI. The recursive formulation is used to analyze the Ramsey growth problem using dynamic programming techniques.

We examine the role of commercial banks in monetary transmission in a factor-augmented vector autoregression (FAVAR). A FAVAR exploits a large number of macroeconomic indicators to identify monetary policy shocks, and we add commonly used lending aggregates and lending data at the bank level....

Recursive least squares learning is a central concept employed in selecting amongst competing outcomes of dynamic stochastic economic models. In employing least squares estimators, such learning relies on the assumption of a symmetric loss function defined over estimation errors. Within a...